{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pythreejs as p3\n",
    "from IPython.display import display, Image, clear_output\n",
    "from pyntcloud import PyntCloud\n",
    "import pandas as pd\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 读取二进制文件, 转换为十六进制字符串\n",
    "def bin_to_hex(input_file, output_file):\n",
    "    \"\"\"\n",
    "    将小端字节序的二进制文件转换为十六进制文本文件，每8个字符换一行。\n",
    "    :param input_file: 输入的二进制文件名\n",
    "    :param output_file: 输出的十六进制文本文件名\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 打开输入的二进制文件和输出的文本文件\n",
    "        with open(input_file, 'rb') as bin_file, open(output_file, 'w') as hex_file:\n",
    "            # 读取整个二进制文件\n",
    "            bin_data = bin_file.read()\n",
    "            \n",
    "            # 遍历每个字节，将字节转换为小端的十六进制字符串\n",
    "            hex_data = bin_data.hex()\n",
    "\n",
    "            # 将每8个字符（对应4个字节）换一行\n",
    "            for i in range(0, len(hex_data), 8):\n",
    "                reversed_chunk = hex_data[i:i+8][::-1]  # 反转每个4字节（8个十六进制字符）\n",
    "                swapped_chunk = ''.join([reversed_chunk[j+1] + reversed_chunk[j] if j % 2 == 0 else '' \n",
    "                                         for j in range(0, len(reversed_chunk), 2)])\n",
    "                # 将交换后的内容写入输出文件\n",
    "                hex_file.write(swapped_chunk + '\\n')\n",
    "                # hex_file.write(reversed_chunk + '\\n')\n",
    "\n",
    "        print(f\"成功将 {input_file} 转换为 {output_file}.\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"发生错误: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 定义处理原始数据的函数\n",
    "def process_raw_file(file_path):\n",
    "    matrix = []\n",
    "    with open(file_path, 'rb') as bin_file:\n",
    "        # 读取整个二进制文件\n",
    "        bin_data = bin_file.read()\n",
    "        \n",
    "        # 遍历每个字节，将字节转换为小端的十六进制字符串\n",
    "        hex_data = bin_data.hex()\n",
    "        # 将每8个字符（对应4个字节）换一行\n",
    "        l = 0\n",
    "        for i in range(0, len(hex_data), 8):\n",
    "            reversed_chunk = hex_data[i:i+8][::-1]  # 反转每个4字节（8个十六进制字符）\n",
    "            swapped_chunk = ''.join([reversed_chunk[j+1] + reversed_chunk[j] if j % 2 == 0 else '' \n",
    "                                    for j in range(0, len(reversed_chunk), 2)])\n",
    "            # 将交换后的内容写入输出文件\n",
    "            distance = int(swapped_chunk,16)\n",
    "            matrix.append(distance)\n",
    "            l += 1\n",
    "            if l == 256*32:\n",
    "                break\n",
    "        # 转换为NumPy矩阵\n",
    "    return np.array(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 定义绘制range view图像的函数\n",
    "def plot_range_view(distance):\n",
    "    distance = distance.reshape(32, 256)\n",
    "    # 画出range view，256*64，保持横纵比，和照片一样\n",
    "    plt.figure(1)\n",
    "    plt.imshow(distance, aspect='auto', cmap='jet')  # 转置并显示图像\n",
    "    plt.axis('equal')  # 保持横纵比\n",
    "    plt.axis('off')    # 关闭坐标轴\n",
    "    # 保存图片\n",
    "    output_file = \"output_range_view.png\"  # 你可以替换为其他文件名\n",
    "    plt.savefig(output_file, dpi=300, bbox_inches='tight', pad_inches=0)  # 高分辨率保存\n",
    "    print(f\"图片已保存到: {output_file}\")\n",
    "    # 显示图像\n",
    "    display(Image(filename=output_file))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. 定义处理点云数据的函数\n",
    "def process_point_cloud_file(file_path):\n",
    "    matrix_label = []\n",
    "    matrix_xyz = []\n",
    "    # 读取文件\n",
    "    with open(file_path, 'r') as f:\n",
    "        l = 0\n",
    "        for line in f:\n",
    "            line = line.strip()  # 去除行末的空白字符\n",
    "            if l < 256*32:\n",
    "                label = int(line,16)\n",
    "                matrix_label.append(label)\n",
    "            elif l < 256*32 + 256*32:\n",
    "                x_hex = line[:3]    # 前3位为x\n",
    "                y_hex = line[3:6]   # 中间3位为y\n",
    "                z_hex = line[6:8]   # 最后2位为z\n",
    "                # z_hex扩展为3位，符号位扩展\n",
    "                if z_hex[0] >= '8':\n",
    "                    z_hex = 'f' + z_hex\n",
    "                else:\n",
    "                    z_hex = '0' + z_hex\n",
    "                x = int(x_hex, 16)\n",
    "                y = int(y_hex, 16)\n",
    "                z = int(z_hex, 16)\n",
    "                # 有符号数转换\n",
    "                if x >= 2048:\n",
    "                    x = x - 4096\n",
    "                if y >= 2048:\n",
    "                    y = y - 4096\n",
    "                if z >= 2048:\n",
    "                    z = z - 4096\n",
    "                matrix_xyz.append([x, y, z])\n",
    "            else:\n",
    "                break\n",
    "            l += 1\n",
    "    # 转换为NumPy矩阵\n",
    "    return np.array(matrix_xyz)*0.04, np.array(matrix_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 使用PyntCloud绘制3D点云的函数\n",
    "def plot_raw_xyz(xyz, label):\n",
    "    df = pd.DataFrame(xyz, columns=[\"x\", \"y\", \"z\"])\n",
    "    # df[\"size\"] = np.ones(len(df)) * 0.01\n",
    "    cloud = PyntCloud(df)\n",
    "    # 可视化点云\n",
    "    cloud.plot(initial_point_size=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. 删除噪点后绘制3D点云的函数\n",
    "def plot_denoise_xyz(xyz, label):\n",
    "    # 删除噪点\n",
    "    xyz = xyz[label != 0]\n",
    "    df = pd.DataFrame(xyz, columns=[\"x\", \"y\", \"z\"])\n",
    "    # df[\"size\"] = np.ones(len(df)) * 0.01\n",
    "    cloud = PyntCloud(df)\n",
    "    # 可视化点云\n",
    "    cloud.plot(initial_point_size=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. 标记碰撞点后绘制3D点云的函数\n",
    "def plot_crash_xyz(xyz, label):\n",
    "    # 删除噪点\n",
    "    xyz = xyz[label != 0]\n",
    "    label = label[label != 0]\n",
    "    colors = np.zeros((len(label), 3))\n",
    "    # 标记碰撞点\n",
    "    for i in range(len(label)):\n",
    "        # 十位数为1的是碰撞点\n",
    "        if (label[i] // 10) % 10 == 1:\n",
    "            colors[i] = [255, 200, 180]\n",
    "            # colors[i] = [155, 20, 10]\n",
    "        else:\n",
    "            colors[i] = [223, 114, 21]\n",
    "    df = pd.DataFrame(xyz, columns=[\"x\", \"y\", \"z\"])\n",
    "    df[\"red\"] = colors[:, 0]\n",
    "    df[\"green\"] = colors[:, 1]\n",
    "    df[\"blue\"] = colors[:, 2]\n",
    "    cloud = PyntCloud(df)\n",
    "    # 可视化点云\n",
    "    # cloud.plot()\n",
    "    cloud.plot(initial_point_size=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eb15af9433b40cd85e71d71739cb0d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='初始化', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3b26783f57c4dd9982ba8495c9e70e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='原始深度图', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5233d33bc64f4d229b0227c931f7636f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='点云转换', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2993ed0c88854304bf82100992b62ec0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='点云去噪', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3c6b5cefa384faaa94deeb722c92058",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='碰撞预警', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片已保存到: output_range_view.png\n"
     ]
    },
    {
     "data": {
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37rVF/Xga+4U49J3rPxHrX/0/8rk4DuSD9+/+qa2v9Z+v/qfc44Xbc/2hg8WcuVzez8+huj+r7uemUE+1McY4VtSr53Pg33TrlOufLOqcuTum3vjqul1dF9M1t7rezqV6zrsp1Kvv4bnyxpSJVGOre4VO72r8oSKCPr0qer9W9H49lg8eejXW33zo21tq5xdjzz9v69gx/AIdAAAAAAAiAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACBYjXEib4Fa7dydVLt5d3qMUe/o+lToc3nRO43dzhXN8cmTRY+rmr2r179kj0emXL821B8qxl5X1CvdNW7ifemMn2N9Y+Qdmm8MtTHGON3s3R3f6fHAlOvdz0VnjdV7/mAx5/VFvXPsOjtLb+fef1D84eDW0i3v781Z7Qr/saKe+iy9E/kNoX917KvXeao55xzHudoVvXJRc3zHHOdzZenr84tTY/CHc/mJP5PrxbH7rvf+sy2118d5cey/XD2wk4Vt1DXrPxvrbx3fjPW3hPoF4xtFj2/F+vkj70R/YLwe6/9o/NCW2m+tfi2Ofc/6qlifw5dX1V8ej9VPr1+M9erzUumMP12Mfb04uareqX5ecXwq1fjzGhed6jNR9b5l9W/nRo9Nud65/l1T9Kike98x6vvfTTg+ba29svCc1b1Vel+63+fd8dUxCt61vnrHY7fzldVjZ96kug9b0kNFvXpur56t0zFa8j6kMtfzVrfPU1/YWrv8B+fpXZmjzxNTrnfyiWod1fV5Lp1nhe5z6CWNdSz9vN0Zv5fOuW6903/J3ntpzrnW0q2nZ6Vq7OGiPkd++lyzx6UzzFn4jlM3xfr5b3411v0CHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEKzGsXXeRHQOc22kkVSbhc7xn9p3VZuIwn5QbdBZbeqypGrDmGoDrCVVm5dVm8CkTW26G4NUm4hW7gjjn+y1aG8MVm1qtFdUm3++VNSPFvUXivocLix6v1zUq/FLqjZjTaqNRasNY6rP3LPF+JtDvVpft17ZxGZPlWnaWkvn/hjj+/7rGTbLG2P8k1V6w56YpfccPrj+92O92riz0tkstLsRabW5aEe1oWdlrk1Hk0dWWzfzHWOM8eJtuf5M86S75tFQ/FIemzbiHGOMl4ve1fnc2Ujv6sbY7aQ55+rd1Xn99xVjq03Bq3raMLU6PlWP6l7plqKe+txbjO2aY+PaaoPSh3pLad3PLf0dt2SGsJc2EZ3jdS65cWl1jlfXnO5aHin6pPNiL23mzO5L97Lb2UubiFa3MzdOW2v3h9p2vbtzdl5/tYnoHHMeakbQB4ovnaMfz0s5+RM7bn3qyKdaS/ELdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAYDXGieYWqHvE5VOud3fLfbLoc8UM/avd37s7l1/YWEe1m/Ucrit6P1jUry/qDxT1G4p60j3O94cd1McY48ZiF/U55qx2Ir57yvWbQr37WVly/NJrmUM15xw7UVeq3bKvKeqVo6F2T7NH5Zaiz7OhNtdxe6KYs7oudMz1WXwq1F6ams1ncEkx5/NF/cKifknR/6lifHK0GFu9h9X33MuNOS8uxr5Y9XhvLl///p3PWam+n84GN065fjjU7v7DoskLsXrx+i2x/uLql2P9T62vL/pvdf74dqy/Ot684x6V18d5Z9xjjDFOz9Qneb39pbNz/9fqN2P9ovVfnKX/gfH6llr1mai86aW/Get/dNHfeUNr2pFrp9746jq31z025frVRb1SfUTT90I155KmYs7quHWfz1K96lHdE1bPUJcXfW4txrO3VflBlTfsddW1optDdK+56b6lUp23DzyR6zdc2VtL8pkp148X9SNFn87zTPVMmPKDN2KOZ+JqfKdPNfZjU28t7Fx13nazjCW9eFuu31WEPJ3PYjW2aO0X6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAwWocW68X697duXcOT0298Uvull31LnZ0HY/PMGfl3UXvZ2aY85KiR2c3++10xr/02Vw//pFcv3/aee/uLtf3FmsZX+312S/SDu0PfaEY/BtLrmQzbp621qod168u6tW5Ul1brgr1ztjtxm/CdVOuP1GMf7kYn1TXuecbPbq9q+N5uKh3vnO71+GTRf2Vol6t8cVpa+3iUKvGjjHGpUW9ev3PFuPPNTdOuX5fUe94/kSuH2h8kI5+vNd7SadXC/dftv1iLpty/bmi3nGg+bjxfHGMOveWjxRjq3PimqLeOZ6PFT0q1fd52f9zofg7vR6Vai3Va0rv1/Gi96lmvbqe3RXq3d7V8fxkMWe6/7+3GNuV7n3HqNeeVMfnWFHvPp+n92vpa1xa49Ppsz/GOPZXcr0af3kxvmOujGOOPptYS/c613VD6P+Z5pzVM0H1Oh9o9E/rG6O/xiW1c4vm+KXcMeV693NejU/1ztil11LlhFWP7vi7Q+3mYux9zd5V/cGp+ENwvBhbZXbV57z6PN8a6s330C/QAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAACC1Ti2Xi/WvdqJ9akp1y8v6nP0mGuH6o7uDuWHi/rLM/Q+WtQfmZqNZnBxc84Xm+M7jjR6n2yMHWOMw8X4zrE7VfQ4VNSvb/TurqX7mVtyfHU+Vz06uytXvavdn68p6pWjRf2eR7fWbnp/Hvt8b8ryfXli2lq7MtSqsWOMcX1Rn8MDRe8Li/rLRb2SrkXd74oXmnMmR2foMUa9lstC/dlibPf6fLKoHynq6Xp+UTHnS7+Q60d/LNer158+o93r0yZ0r2fVPUTHfdMMTbqK69wI10TOYj9Z1H92uSlvm3L9uWL8qWb/dI5u4nljL6lef/d54+5ifDpG1XGbih63FvXqmpv631tcn64vrmfVGrvX+ZONHq8U9aen4g+FY2H80t+hmziP5phzrnXvlbV0e8w1Pt3PzPVcWc2ZnjluCLXtdF9/575t6dffGTvXce48h1c6z/iVJV/PGGPcPDUnaKielavn86tCvVr3Y0WPKvuo+qRj1H0Pq3r13HJTUU/nXNX7rtzDL9ABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAILVGCfW8S9px+0x8s7d1dhqR9OnivGXF/WOpXftfnJaeIJddumU688V9eRoMfbyYny1W3C10/VnUr0Y2653NHscbo5/ZYY5K8ebfdLO3d3dvzu951L17uxyXonHZ5s5K9U16uJQu2fq9U47a48xxuPNPsmVRY8ninq1+3dSXRM4e3Wvf7FHUU+7uXfrz0x5bLWzfNV7E7qvvzP2kqJ+y5Trdxf1OXTv55b8bulK71f1Xs31HbJXdNd385Tr3e+/5MWi/lKz/tgMazmbXT3lenpfqmvou+dZyvjYZ7fW7vhIHludW936qcbYk0X9wan4Q6F6H5O0vjHqtVTX/877cjZct5acs+pd1at75ZZ3FvWvztD7HHTdlOvVuZieW6rnk1uL+svFWu4vxqd7hercuudLuX7Te4t/0HRvWEtX9b50zsXuudWtb2LOTn3J1zlGfV50XFv0eKjRu8r9qtdTnUPde8j0GW2+t36BDgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQrMax9Xqx7tWOpk9Ni03ZdsWU608W9TS+GtvZtX477w7jn2n2GCeK+qqod/vvdVMuX1gMj7toFz0qB4rxp4t6NT6OLeqnGj3OZt2d1c8G1fWio9q5fUmPT7medrMfI+9of6gYu19U5+3Z8L60rznvDLWvzrCQMcYlU64fCrWLix6XFvW7i95zWPp6dlPoc+9MveFM3Tbl+l1FfUnXFHNu4rt1SdU95CPTmfe+tuhRXXMre+k9T2u57xfy2Ot/bOc9tqs/NOV6uldM33Hb9aiefTtrfLrocS6q3q+O6pybw1y9O32qsXOt5XCoda8J1fjqfEnj0zPLGGPcUNSr139/MR5221XT1lr3fJ6jPlfv+6biD4V0z9m83/QLdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQLAa40TeRPTYtPMu3Q0jnvpm8x90HGyO/3hRvz2Xrwj9n3yt1/vyKderzVXjJqLNvV8vKzYLfbb6B2ktqfYG6keL4S8U4y8L9WerOSvF+IuK4S+F2pFi7MlqLc36kpuI3tjoPcYYp0Kt2oylWkt3w6Q5dNfYGdtd95IbBqXjs119yfe82kS02gCzs5Y5NtxdWrXGzvk/1+sp1xJ05+xecw4X9c5GMtVGT1W96vNcWEt3A6iuao0de2kTPbI5jtFcn7m0kVL3u39JS34nzuXF5vh0nleb01c28b3dNcf93Nlw/Od4zx8sNhcdX5uheVP1LF9tAFo9n6b3pXqvnil6dHKFMcZ4+guhxw/msUt/tqrn8z3jHUV9A5+5jXhPUf/yrq5iY6rv+e7mitGfK+q/M0NvlvWBov7wmbeuNgs/WoxPuVp1rzTH5r/bSd8XzedKv0AHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAhWY/zBOv+psaX1sbeecYttPfXNUPxEHlvtIP7Ua8X4g821pP63F4M/nsvlrujFoXj3amvtmWJs6c7e8EumrbXnm1POZlqwR7d+pmPHGDcX4+9p9tltN0y5/pn7i3/wwjLrGKPe/bm7E/NSPcaY5/r3yJTrVxf17pxV/zkcaPSe6z2nMG0tVcfndFHvOlz0SZ/Rk8XYixs9xqh3UU/1o8XY6rNYnSvVdXHJz/QDxZzXF/VN2MQ5/eC0tXZdqO01ad1jbGbtDz5Z/OHxXJ7jM/fAl4re7232mUKPUNvOqaL+UNEn9Z/r2adaSzq39tJ36F5ay1w28ZrmmPOVol59V1ZzzvGZm+O8mOvc2sS8S659rt6pz2PTTM2BfatzL1Zdz6r6/UXvG2eYs+AX6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAwWqMf7XOf/pELl8yba09H2pjjDF+qrmcYs6W23vDLz2Y689NO++R3pPtPF+85ZVLV1trz32pGPzdRf3O3pxLunjK9WpH95fS+KLHeLKoX1HUqz4Nh5s9Tp35lOVuwTcU9eq9rXTG76XeD/1Crl/zY8vNuWSfJd/brqea409PxR+q+rlmKupvC7WfWHAdTd3dz5s7l49DjR5p7Hb1qs+F265oZ6pz68kp168o6uyu/X58zsXX373m7Gdz3G92LXkfcjZ4+v5cP3Z8nv775f19JtzPv7txL/9G7Pdry9OfCsUiJzn20UWX0vL0NFOjd4baW4qxX55pzv3ivUW9yrPY264s6k/svEX3PnSu6/MMffwCHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgWI1xYt37Jz/VGPuJXL5o6k350ms7H3tRsVv0S805x+2Nsc3tXC9a5Xp7jUm3x2dz+eKPbK29WLS4uKhX4y8q6i8V9ZYply8s6i8XbY6E2smix4Gifrp4bw+F93aMMU4Xa4lzNsZu5+qifirUHpvy2KuKevV6Oq/zyaL35UW90plzrh5zzDmXOdbyfNV7Kv5Q1feSaYe1OX0q1H6i2WNq1huqa8uS9eo7pLKXzq39zrHYfc89WvzhD7eWLi3uN5Y01/3JfvHslOuXFfU5VMdoyfO56v3ctOCktFwy7f6cczz7VD2616K5+nQ8P+X6Jo5FUq2PDXhHUf/arq4C+q4MtSeWnfLSKddnuOfwC3QAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgGA1Dq/XrX/xyrS1djjUtu3xzVw//NYF53ytN/7IwVw/GeY9Uqwljd12fO9QZHfO0KOp+/rn6FOOrXoX9VNFvbPjetXjUHN8cnrK9QNFvVK9nlNFn2rn4jlUu9wvaRNzdu31Nb7UHF++nmmHtbPF1KzvcdW1orqeVS5qjH1+yvWjRf1s9cKU6xcXdbIXP5vrF39kd9fBsl78uVy/+KNn3ru6zr3whVw/+oNnPifz2Ov3Si9OxR/eWdS/msud74W9/p6cLTrv48tTr/dFzfFzeGkDc3KOeU9R//KurgIW1XwO8wt0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAsBrjRN65stqkMG5qeHtv1gPFBp3VholRNWexM9CBVTFntXFntRnniTPvXY2vxPcl1bZTjC+PcxpbtG5tFrjdWrr9Gzqbgp7N9tLr3Etr4cztpeNZXRNe2dVVnD06x64ae7g5p03NOJd0P89LXi+7G9Rz5k4+mutH3r+761jaXvqe7/B9k51r78srU2/84eb4V/6Xos+fb/RozgmLeXtR//qurgLONX6BDgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQrMY4sT7zNtNM9deK+sdn6HGwuZaqvlQP5jE1650+c/R4I332i2mHtYUdWLj/6amYt6jvdd3Xk8aXYz9V9P6Jop7Lszg15fqhor6k7us8NcOc1XHexOs/15ze9AJ24Fy7bs1lE+/LfjkW3etcOo+W/j5nOdWxOxuul3PY66+zug6ND1f/oKhXz+2dHo82egDAG+cX6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAwWqME+vePzkRancWY6fmcqrxac6ltyevtn9Pr3VacB1kU7O+X0zN+pJzzjV+jt5Lzsnum5r1vWTaYW0/ub+oH9/FNXD2+GxR/8iuruLsMDXr7H3TDmtsxv1F/fgurmFuX2qMrZ6fH55jIQCwUX6BDgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQrMY4sV6u/e3LtV5ctYt4cnqGHmezO4v6iQ314dyxWrj/tMNat8cb6cNWU7O+pGrOqg6whPuL+vFdXANsyrTD2ly95+w/hynUPlCMfXjBdQDA/uMX6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAwWqME+tNL4Jz1TuK+td2dRX7y58r6r+zq6vgbHb7phfwrzlQ1O8s6iea/U83x+9nv1zUf2RXV/HGfDzU9tLnHGAvS9fQMfJ1tBoL+8Hbivrv7+oqAFiGX6ADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAACChTcR/RtF/eByUy7uZxtjf3KGHtv1maP3uab7ns9xjDrHZ2nd1zmHXy3qH1pwzspni/rZsNEhO/eJov5TM42Hvaza/LbadDeN74xlM+a4V6yOc6X72YLd9nCofWDXVzGf6pnYZqwAsNf4BToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQLAa48R604tgL/obofbpXV8FsNt+cqY+r83UB8511Xdr+h7ejT4A57KDG5jzZ4t6dc9Vjf9QqHXvtx5ujgcAxvALdAAAAAAAiAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAYDXGifWmF/HH++imFwBvwM8VdZ/n5RzY9AJ2yac3vQDesLeF2u8XY10rAN646j7sbOZ7YX/4+gw9Xivqp4v6ozPMCUv5QFF/eFdXAexvfoEOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABCsxjix3vQi5nXjphfwr7mvqC+5xk3Mud8dLOqvNccv1eNs9nNF/aMzjWc51Wd3E/bLdXG/vM69xHsO5z7nOTtVfVa6PtQY273fOt0c/2hzPACcm/wCHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgWI1xYr3pRfx/fnTTCwBKB4v6fUX9xqUWsqE5z0WvbXoB57BfKur75Xtuv7/+pHpPKvv5vYLTRf3Arq4Csu71fC4faozt3uNV51zyaLM3AJz9/AIdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACBYjXFivelFvDE/sukFsGvSLvIHd30VHNj0Ajbs9KYXwBv2eFG/aldX0ffLm17ALul+n++X9wXg/8/zz/6Wnomq+9M0djud+9xHm70B4OznF+gAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIDgLN5EtOvDRf1XmuP3urleT2eTzu4mNdUa59gYqbsWm5FmaZO+TWxcdbZu/rjXVOdF+vx3z6G95Fy7np+LqmMEcA64etr9OR+ba87qPm+/b9y81+8hupvc/+oiqwCAc51foAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABKsxTqw3vYg/XtoV/rVdXwWwV/zKphdwBj686QX8Mc7m9/ZDRf1Xi/qSx6J6Hx1/OPe9LdR+f9dXAftD9b16uqgfWGohM+rcQ1Svs1I9Qz/c7AMA+4tfoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABKv1er3e9CIAAAAAAGCv8Qt0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4AAAAAAIEAHQAAAAAAAgE6AAAAAAAEAnQAAAAAAAgE6AAAAAAAEAjQAQAAAAAgEKADAAAAAEAgQAcAAAAAgECADgAAAAAAgQAdAAAAAAACAToAAAAAAAQCdAAAAAAACAToAAAAAAAQCNABAAAAACAQoAMAAAAAQCBABwAAAACAQIAOAAAAAACBAB0AAAAAAAIBOgAAAAAABAJ0AAAAAAAIBOgAAAAAABAI0AEAAAAAIBCgAwAAAABAIEAHAAAAAIBAgA4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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def init(b):\n",
    "    input_file = \"output.bin\"\n",
    "    output_file = \"output.mem\"\n",
    "\n",
    "    # 调用函数进行文件转换\n",
    "    bin_to_hex(input_file, output_file)\n",
    "def rv(b):\n",
    "    clear_output()\n",
    "    display(button1, button2, button3, button4, button5)\n",
    "    file_path = \"bin_data1.bin\"  # 你可以替换为其他文件名\n",
    "    distance = process_raw_file(file_path)\n",
    "    plot_range_view(distance)\n",
    "def pc(b):\n",
    "    clear_output()\n",
    "    display(button1, button2, button3, button4, button5)\n",
    "\n",
    "    file_path = \"output.mem\"  # 你可以替换为其他文件名\n",
    "    xyz, label = process_point_cloud_file(file_path)\n",
    "    # 删除所有无效的点\n",
    "    xyz = xyz[label < 1000]\n",
    "    label = label[label < 1000]\n",
    "    # 绘制2D转3D的点云\n",
    "    plot_raw_xyz(xyz, label)\n",
    "def pc_denoise(b):\n",
    "    clear_output()\n",
    "    display(button1, button2, button3, button4, button5)\n",
    "\n",
    "    file_path = \"output.mem\"  # 你可以替换为其他文件名\n",
    "    xyz, label = process_point_cloud_file(file_path)\n",
    "    # 删除所有无效的点\n",
    "    xyz = xyz[label < 1000]\n",
    "    label = label[label < 1000]\n",
    "    # 绘制去噪后的点云\n",
    "    plot_denoise_xyz(xyz, label)\n",
    "def pc_crash(b):\n",
    "    clear_output()\n",
    "    display(button1, button2, button3, button4, button5)\n",
    "\n",
    "    file_path = \"output.mem\"  # 你可以替换为其他文件名\n",
    "    xyz, label = process_point_cloud_file(file_path)\n",
    "    # 删除所有无效的点\n",
    "    xyz = xyz[label < 1000]\n",
    "    label = label[label < 1000]\n",
    "    # 绘制碰撞点\n",
    "    plot_crash_xyz(xyz, label)\n",
    "\n",
    "button1 = widgets.Button(description=\"初始化\")\n",
    "button1.on_click(init)\n",
    "button2 = widgets.Button(description=\"原始深度图\")\n",
    "button2.on_click(rv)\n",
    "button3 = widgets.Button(description=\"点云转换\")\n",
    "button3.on_click(pc)\n",
    "button4 = widgets.Button(description=\"点云去噪\")\n",
    "button4.on_click(pc_denoise)\n",
    "button5 = widgets.Button(description=\"碰撞预警\")\n",
    "button5.on_click(pc_crash)\n",
    "display(button1, button2, button3, button4, button5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功将 output.bin 转换为 output.mem.\n"
     ]
    }
   ],
   "source": [
    "input_file = \"output.bin\"\n",
    "output_file = \"output.mem\"\n",
    "\n",
    "# 调用函数进行文件转换\n",
    "bin_to_hex(input_file, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "图片已保存到: output_range_view.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_path = \"bin_data1.bin\"  # 你可以替换为其他文件名\n",
    "distance = process_raw_file(file_path)\n",
    "plot_range_view(distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"output.mem\"  # 你可以替换为其他文件名\n",
    "xyz, label = process_point_cloud_file(file_path)\n",
    "# 删除所有无效的点\n",
    "xyz = xyz[label < 1000]\n",
    "label = label[label < 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/capsule/home/jpzheng/anaconda3/envs/aipcc/lib/python3.8/site-packages/pythreejs/traits.py:257: UserWarning: 64-bit data types not supported for WebGL data, casting to 32-bit.\n",
      "  warnings.warn('64-bit data types not supported for WebGL '\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "530bc7beeece4e3fbd554f5d3426c078",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Renderer(camera=PerspectiveCamera(aspect=1.6, fov=90.0, position=(9.647891936144074, 15.00592713876382, 2.1680…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "527b99a04da24164a40acb6258ff7e37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Label(value='Point size:'), FloatSlider(value=0.001, max=0.01, step=1e-05), Label(value='Backgr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制2D转3D的点云\n",
    "plot_raw_xyz(xyz, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/capsule/home/jpzheng/anaconda3/envs/aipcc/lib/python3.8/site-packages/pythreejs/traits.py:257: UserWarning: 64-bit data types not supported for WebGL data, casting to 32-bit.\n",
      "  warnings.warn('64-bit data types not supported for WebGL '\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b6e7c114a894b46ab264c2095e038dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Renderer(camera=PerspectiveCamera(aspect=1.6, fov=90.0, position=(9.69036598265284, 15.017998730696007, 2.1433…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc5788dfc3af4bb68e4d4158765c85c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Label(value='Point size:'), FloatSlider(value=0.001, max=0.01, step=1e-05), Label(value='Backgr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制去噪后的点云\n",
    "plot_denoise_xyz(xyz, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/capsule/home/jpzheng/anaconda3/envs/aipcc/lib/python3.8/site-packages/pythreejs/traits.py:257: UserWarning: 64-bit data types not supported for WebGL data, casting to 32-bit.\n",
      "  warnings.warn('64-bit data types not supported for WebGL '\n"
     ]
    },
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       "model_id": "0c0ffdd43f4e48618e60ab5cfed3432c",
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      },
      "text/plain": [
       "Renderer(camera=PerspectiveCamera(aspect=1.6, fov=90.0, position=(9.690365982652846, 15.017998730696002, 2.143…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      },
      "text/plain": [
       "HBox(children=(Label(value='Point size:'), FloatSlider(value=0.05, max=0.5, step=0.0005), Label(value='Backgro…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制标记碰撞点后的点云\n",
    "plot_crash_xyz(xyz, label)"
   ]
  }
 ],
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